Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

ABSTRACT:

Background

People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.

Methods

In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potent

SUBMITTER: Shi Y 

PROVIDER: S-EPMC10447970 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature